INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has caused significant mental distress in populations globally. At the frontline of the pandemic, emergency departments (EDs) are the prime setting to observe the effects of the pandemic on the mental health of the population. We aimed to describe the trend of mental health-related ED attendances at an acute hospital in Singapore before and during the various stages of the COVID-19 pandemic. METHODS: This is a retrospective, descriptive study of patients who presented to the ED between 1 January 2019 and 31 December 2020. Patients diagnosed with mental health-related systematised nomenclature of medicine who visited the ED during this period were identified and were placed into mental health diagnosis categories for analysis. A comparison was made between patients who presented before the pandemic (2019) and during the pandemic (2020). RESULTS: During the study periods, we identified 1,421 patients, of whom 27 were excluded due to non-mental health-related diagnoses, leaving 1,394 patients for analysis. There was a 36.7% increase in mental health-related ED presentations from 2019 to 2020. The proportion of higher-acuity mental health-related ED attendances and number of suicide attempts also increased. CONCLUSION Our study described an increase in the proportion of high-acuity mental health-related ED attendances during the COVID-19 pandemic. Emergency physicians must be cognisant of the effects of the pandemic on mental health. Further research should be conducted to better equip the healthcare system for handling all aspects of the pandemic.
Humans ; COVID-19/psychology* ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Singapore/epidemiology* ; Male ; Female ; Adult ; Middle Aged ; Mental Disorders/diagnosis* ; Mental Health ; SARS-CoV-2 ; Tertiary Care Centers ; Pandemics ; Aged ; Suicide, Attempted/statistics & numerical data* ; Young Adult ; Adolescent

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

Psychogenic non-epileptic seizures are accompanied by motor,behavioral,sensory,and/or cognitive changes,with the clinical manifestations similar to epileptic seizures.This disease is easy to be misdiagnosed and neglected in clinical work.At present,most intervention measures still depend on the experience of clinicians.This article reviews the comorbidities of psychogenic non-epileptic seizures,including mental and cognitive disorders,somatic syndrome,sleep disorders,and epilepsy.This review aims to strengthen the precision of clinical treatment and management of patients with psychogenic non-epileptic seizures and provide more efficient individualized diagnosis and treatment programs for patients.
Humans ; Seizures/diagnosis* ; Comorbidity ; Epilepsy ; Sleep Wake Disorders ; Mental Disorders ; Psychophysiologic Disorders ; Cognition Disorders

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Humans ; Mental Disorders/diagnosis* ; Alzheimer Disease/diagnosis* ; Brain Injuries ; Electroencephalography ; Recognition, Psychology

Research on facial micro-expression analysis has been going on for decades. Micro-expression can reflect the true emotions of individuals, and it has important application value in assisting auxiliary diagnosis and disease monitoring of mental disorders. In recent years, the development of artificial intelligence and big data technology has made the automatic recognition of micro-expressions possible, which will make micro-expression analysis more convenient and more widely used. This paper reviews the development of facial micro-expression analysis and its application in forensic psychiatry, to look into further application prospects and development direction.
Humans ; Forensic Psychiatry ; Artificial Intelligence ; Mental Disorders/diagnosis* ; Facial Expression ; Emotions

OBJECTIVES: Adolescents are at high risk of non-suicidal self-injury (NSSI). Currently, there is no clinical assessment tool for adolescent NSSI behaviors measurement with global consistency. The Ottawa Self-injury Inventory (OSI) is considered as a relatively comprehensive assessment tool for NSSI, but the questionnaire is discussed with excessive content and timecostly, which may affect the reliability of the measurement results for adolescent.Thus, this study, based on OSI, aims to revise the assessment tool for adolescent with NSSI that is suitable for both clinically and scientifically, referring to the diagnostic criteria for NSSI in the 5th Diagnostic and Statistical Manual of Mental Disorder (DSM-5). METHODS: This study was led by the Second Xiangya Hospital of Central South University and collaborated with 6 mental health service institutions in China from August to December 2020. Adolescent aged from 12 to 24 years old who had self-injury behavior and met the DSM-5 diagnostic criteria for NSSI were continuously recruited in the psychiatric outpatient department or ward. After clinical diagnosis by an experienced attending psychiatrist or above, the general information and OSI were collected by questionnaires. SPSS 24.0 and AMOS structural equation model statistical softwares were used to conduct item analysis and exploratory factor analysis on the obtained data to complete the revision of the scale. Cronbach's alpha coefficient, split-half reliability, test-retest reliability, and content validity and structure validity were performed to analyze the reliability and validity and confirmatory factor analysis was carried out to test the structure validity for the revised scale. RESULTS: A total of 234 adolescent with NSSI were enrolled, including 33 (14.1%) males and 201 (85.9%) females with the mean age of (16.2±2.6) years old. The most common clinical diagnoses were depression disorder (57.4%), bipolar disorder (20.9%), adolescent mood disorder (17.1%), etc. Nine items (item 2, 7, 11, 13, 23, 24, 10, 17, 18) in the functional scale of OSI were deleted according to extreme grouping method, correlation analysis, and principal component analysis in exploratory factor analysis. The revised functional scale for NSSI consisted of 15 items. The reliability analysis showed that the Cronbach's alpha coefficients of NSSI thought and behavior frequency, addiction characteristics, and function scales were 0.799, 0.798, and 0.835, respectively, and the split-half coefficients were 0.714, 0.727, and 0.852, respectively. The test-retest coefficients of the latter 2 scales were 0.466 and 0.560, respectively. The correlation coefficient between sub-items and total scores in each part of the scale showed good content validity. The exploratory factor analysis showed that a component was extracted from the frequency of thoughts and behaviors of NSSI, one component was extracted from the addictive characteristics, and three components were extracted from the functional part. The three functional subscales were social influence, external emotion regulation, and internal emotion regulation. The factor load of each item was >0.400. CONCLUSIONS The revised Chinese version OSI targeted the adolescent patients with mental disorders has relatively ideal reliability and validity. The scale shows high stability, dependability, and a reasonable degree of fit. It is a suitable assessment tool for clinical and scientific research on adolescent with NSSI.
Adolescent ; Adult ; Child ; Factor Analysis, Statistical ; Female ; Humans ; Male ; Mental Disorders/diagnosis* ; Psychometrics/methods* ; Reproducibility of Results ; Self-Injurious Behavior/psychology* ; Surveys and Questionnaires ; Young Adult

OBJECTIVES: To understand the current status of appraisal of post-concussion syndrome disability and the reasons for the changes in re-appraisal opinions. METHODS: The cases that were judged as "post-concussion syndrome and ten-level disability" in the first appraisal and re-appraised for psychiatric impairment by the Academy of Forensic Science in 2019 were analyzed retrospectively. RESULTS: There were 75 cases, including 58 cases with pre-hospital emergency medical records, among which 39 cases were clearly recorded to be without a history of coma; 74 cases had emergency medical records, among which 44 cases were recorded of having a history of coma; 43 cases had follow-up medical records, among which 24 cases had a history of psychiatric follow-up. The most complained symptoms of the appraisee in appraisal and examination include headache, dizziness, poor sleep at night, irritability, memory loss, fatigue and inattention. The main reasons for the re-appraisal application include doubts about the history of coma, doubts about the credibility of mental symptoms, post-concussion syndrome didn't meet the disability criteria, and objections to the original appraisal procedure or the original appraisal agency. The appraisal opinions of a total of 47 cases were changed. Seven of them did not meet the disability criteria, and the main reason was that there was no clear history of coma and no head injury was admitted; the coma history of the 40 other cases had to be confirmed by the court before they can be clearly identified as disabilities. The reason was that the records about the history of coma were inconsistent or there were alterations and additional information. CONCLUSIONS In the past, the conditions for appraisal of post-concussion syndrome disability were too lax and must be further standardized and strictly controlled.
Brain Concussion/diagnosis* ; Craniocerebral Trauma ; Headache/psychology* ; Humans ; Mental Disorders ; Post-Concussion Syndrome/psychology* ; Retrospective Studies

Objective To explore the consistency between identification diagnosis and pre-identification clinical diagnosis of patients with mental disorder undergoing forensic psychiatry identification. Methods The identification data of 1 369 appraised individuals who underwent criminal responsibility identification carried out by the Forensic Institute of Second Affiliated Hospital of Jining Medical University from 2014 to 2017 were collected retrospectively using self-designed investigation data sorting table. A comparative analysis of the mental disorder diagnosis results of expert opinion and past clinical diagnosis results was made. Results Among 1 369 appraised individuals, 964 cases （70.4%） were identified and diagnosed with mental disorder and 405 cases （29.6%） without mental disorder. Among the former, 63.3% （610 cases） were clinically diagnosed, which was higher than 43.2% （175 cases, P<0.05） in the latter. Among the various mental disorders that had been identified and diagnosed, patients with hysteria, stress, and neurosis had the highest proportion of clinical diagnoses （86.7%）, while patients with mental retardation had the lowest proportion of clinical diagnoses （9.6%）. Schizophrenia had the highest overall consistency rate of identification diagnosis and clinical diagnosis （98.4%）, while personality and behavior disorder had the lowest （33.3%）. The overall consistency rate between clinical diagnosis and identification diagnosis of the mental disorder group was 84.1%, and the Kappa value was 0.759. Compared with clinical diagnosis, the consistency rate between inpatient diagnosis and identification diagnosis was higher （85.9%, P<0.05）. Conclusion In forensic psychiatry identification that provides data of past clinical diagnosis and treatment, a high consistency between identification diagnosis and clinical diagnosis of the appraised individual who is identified and diagnosed with mental disorder exists. Clinical diagnosis （especially the inpatient diagnosis） has a relatively good reference value for forensic psychiatry identification.
Criminals ; Expert Testimony ; Forensic Psychiatry ; Humans ; Mental Disorders/diagnosis* ; Psychotic Disorders ; Reproducibility of Results ; Retrospective Studies

OBJECTIVE: Classifying mental disorders on the basis of objective makers might clarify their aetiology, help in making the diagnosis, identify “at risk” individuals, determine the severity of mental illness, and predict the course of the disorder. This study aims to review biological and clinical markers of panic disorder (PD). METHODS: A computerized search was carried out in PubMed and Science Direct using the key words: “marker/biomarker/clinical marker/neurobiology/staging” combined using Boolean AND operator with “panic.” In addition, the reference lists from existing reviews and from the articles retrieved were inspected. Only English language papers published in peer-reviewed journals were included. RESULTS: Structural changes in the amygdala, hippocampus, cerebral blood level in the left occipital cortex, serotonin 5-TH and noradrenergic systems activation, aberrant respiratory regulation, hearth rate variability, blood cells and peripheral blood stem cells, hypothalamic–pituitary–adrenal axis dysregulation were identified as potential candidate biomarkers of PD. Staging was identified as clinical marker of PD. According to the staging model, PD is described as follows: prodromal phase (stage 1); acute phase (stage 2); panic attacks (stage 3); chronic phase (stage 4). CONCLUSION: The clinical utility, sensitivity, specificity, and the predictive value of biomarkers for PD is still questionable. The staging model of PD might be a valid susceptibility, diagnostic, prognostic, and predictive marker of PD. A possible longitudinal model of biological and clinical markers of PD is proposed.
Amygdala ; Biomarkers ; Blood Cells ; Diagnosis ; Hippocampus ; Mental Disorders ; Occipital Lobe ; Panic Disorder ; Panic ; Prodromal Symptoms ; Sensitivity and Specificity ; Serotonin ; Stem Cells

OBJECTIVES: The purpose of this study was to analyze the discriminant validity and the clinical cut off scores of the Child Behavior Checklist 1.5-5 (CBCL 1.5-5) in the diagnosis of autism spectrum disorder (ASD) and non-ASD. METHODS: In total, 104 ASD and 441 non-ASD infants were included in the study. T-test, discriminant analysis, receiver operating characteristic (ROC) curve analysis, and odds ratio analysis were performed on the data. RESULTS: The discriminant validity was confirmed by mean differences and discriminant analysis on the subscales of Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, and Total problems, along with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales between the two groups. ROC analysis showed that the following subscales significantly separated ASD from normal infants: Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Moreover, the clinical cut off score criteria adopted in the Korean-CBCL 1.5-5 were shown to be valid for the subscales Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. CONCLUSION: The subscales of Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems significantly discriminated infants with ASD.
Autism Spectrum Disorder ; Autistic Disorder ; Checklist ; Child ; Child Behavior ; Child ; Diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Infant ; Odds Ratio ; ROC Curve ; Weights and Measures